In recent years, there has been an increasing demand for security cameras and vehicle mounted cameras. As one method for recognizing a target object with a vehicle mounted camera or the like, pattern recognition is used. In the pattern recognition, dictionary data is generated from a feature value of a recognition target object learned in advance. Usually, different dictionary data is generated for each of types of recognition target objects such as a person and a vehicle. Further, a plurality of dictionary data are generated by changing a visual point and a posture of the same type of an object. The object is recognized from photographed images with reference to the plurality of dictionary data generated in this way.
Examples of a representative method of the pattern recognition include a method of recognizing an object referencing an HOG (histograms of oriented gradients) feature value. In this method, a small rectangular region called region of interest (hereinafter referred to as ROI) is set on an image and the HOG feature value is calculated referencing luminance gradient information obtained from pixels included in the ROI. Then, likelihood is calculated from an inner product of the HOG feature value and a plurality of dictionary data prepared in advance. An object included in the ROI is specified by identifying dictionary data having the highest likelihood.
Since there is an enormous amount of dictionary data, a general image recognition apparatus does not include dictionary data inside and reads necessary dictionary data from an external memory or the like every time and performs recognition operation for an object. However, processing efficiency is reduced because a long time is required to acquire the dictionary data from the external memory.